At a Glance
Our client, a leading automotive aftermarket services platform in North America, was seeking to optimise data processing and transformation for its extensive database. Their key focus was on achieving efficient data curation using Microsoft Azure’s cloud capabilities.
The Situation
The customer had a large database which required frequent data updates and accurate transformations. They needed a strong solution to handle data updates. This solution should work for both small and large updates whilst keeping the data fresh and accessible for decision-making.
ROCK guided them towards Microsoft Azure, aiming to leverage the platform for automated data curation and processing, minimising manual effort and improving overall efficiency.
Recommendations
- Develop automated pipelines for efficient data processing
- Implement incremental and full data loads based on the client’s requirements
- Ensure timely and accurate data transformation through Azure Synapse Analytics
How we helped
- Developed over 50 data pipelines using Azure Data Factory to streamline data curation and transformation
- Implemented incremental load pipelines to update data hourly, ensuring data remained up-to-date
- Set up full-load pipelines to handle complete data refreshes on a monthly basis
- Created stored procedures to facilitate smooth data transfer between temporary and target tables
- Automated triggers in Azure Synapse Analytics to ensure timely data loads, reducing the need for manual intervention
Outcomes
The client achieved a significant improvement in data processing efficiency. ROCK set up automated data pipelines for the client. This ensured that their data updated every hour and fully refreshed each month without any manual effort. The client now benefits from:
- Faster decision-making, driven by real-time data availability
- Streamlined data transformation and improved data quality
- A reduction in manual tasks, allowing staff to focus on higher-value activities
- A scalable solution that supports their growing data needs
